modelzoo.vision.pytorch.dit.input.DiffusionBaseProcessor.DiffusionBaseProcessor#
- class modelzoo.vision.pytorch.dit.input.DiffusionBaseProcessor.DiffusionBaseProcessor[source]#
Bases:
object
Methods
check_split_valid
Dataloader returns a dict with keys:
create_dataset
custom_collate_fn
process_transform
- create_dataloader(dataset, is_training=False)[source]#
- Dataloader returns a dict with keys:
“input”: Tensor of shape (batch_size, latent_channels, latent_height, latent_width) “label”: Tensor of shape (batch_size, ) with dropout applied with label_dropout_rate “diffusion_noise”: Tensor of shape (batch_size, latent_channels, latent_height, latent_width)
represents diffusion noise to be applied
- “timestep”: Tensor of shape (batch_size, ) that
indicates the timesteps for each diffusion sample